The Design and Methodology Core will assist investigators to design studies and to analyze data using a mixture of established and innovative techniques to examine questions related to influencing individualization of treatment for women with breast cancer. Core D will support investigators through consultation and collaboration, lead work-in-progress sessions, as well as development and maintenance of centralized resources related to design and methodology. An important goal of our Design and Methodology core is to conduct breast cancer translational biostatistical research, facilitating the movement of new methodological approaches into practice for current and future projects. The project has four specific aims. Aim 1 focuses on the most obvious essential role of directing and supervising the analysis and methods used by the three projects. Aim 2 is designed to promote and support ancillary and follow-on studies that might be suggested from the preliminary work, or new external research findings or policy developments, with the goal of providing additional benefits from the large and expensive data collection effort undertaken to carry out the three projects described. In Aim 3, the investigators propose to do work on two applied statistical problems, identifying cutting edge developments in statistical and biostatistical fields and doing the necessary work to demonstrate that these methods can feasibly and practically answer methodological problems encountered by the projects in this program. Aim 4, is designed to focus on building centralized resources and collaborative bridges, both within the Program projects and cores and more broadly to other groups at UM that are focusing on translational research methods to further enhance our ability to bring the right methods to apply to the right problems. Core D includes experienced personnel with skills in: 1) advanced biostatistical analysis including multilevel modeling, latent variables, missing data and causal inference; 2) psychometric measurement of patient and provider beliefs and attitudes, behaviors, and quality of life; 3) survey methodology; 4) quality measurement and practice profiling; 5) experimental, quasi-experimental and observational designs; 6) technical aspects of managing large administrative and clinical databases.